The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals

Analysis of habitat use based on radio-tagged animals presents difficulties inadequately addressed by current methods. Areas of concern are sampling level, data pooling across individuals, non-independence of habitat proportions, differential habitat use by groups of animals, and arbitrary definition of habitat availability. We advocate proportional habitat use by individual animals as a basis for analysis. Hypothesis testing of such nonstandard multivariate data is done by compositional analysis, which encom- passes all MANOVA/MANCOVA-type linear models. The applications to habitat use range from testing for age class effects or seasonal differences, to examining relationships with food abundance or home range size. We take as an example the comparison of habitat use and availability. The concepts are explained and demonstrated on two data sets, illustrating different methods of treating missing values. We compare utilized with available habitats in two stages, examining home range selection within the overall study area first, then habitat use within the home range. At each stage, assuming that use differs from random, habitats can be ranked according to relative use, and significant between-rank differences located. Compositional analysis is also suited to the analysis of time budgets or diets.

Compositional Analysis of Habitat Use From Animal Radio-Tracking Data

Ninety-three papers on home-range analysis using radio-tracking data were reviewed; these papers were found in a literature search of 18 of the major journals likely to include such papers, published in the 5-year period to the end of 1988. The review showed that even 25 years after the first radio-tracking studies, in the majority of papers there was still insufficient attention given to accurate and sufficient data collection, and to using appropriate and sophisticated analytical techniques to assess home-range size and configuration. This paper is designed to help people undertaking a radio-tracking study to avoid some of the most common pitfalls. It is based on some of the problems we have experienced studying several species of larger mammals. We use our collective experience to produce a guide on how to plan a radio-tracking study, to highlight some of the potential problems in designing the study and collecting the data, and to identify some of the difficulties that may be encountered during the analytical stages. The advantages and disadvantages of the most frequently used methods of home-range analysis are discussed and methods for determining the minimum number of radio-fixes and techniques for adjusting inadequate sample sizes are described, as are the problems that may be caused by autocorrelated data.

Home‐range analysis using radio‐tracking data–a review of problems and techniques particularly as applied to the study of mammals

Aim Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare environmental niches from occurrence and spatial environmental data. Location Europe, North America and South America. Methods The framework applies kernel smoothers to densities of species occurrence in gridded environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with pre-defined distributions and amounts of niche overlap to evaluate several ordination and species distribution modelling techniques for quantifying niche overlap. We illustrate the approach with data on two well-studied invasive species. Results We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of environmental conditions that occur across geographical space. The use of a kernel smoother makes the process of moving from geographical space to multivariate environmental space independent of both sampling effort and arbitrary choice of resolution in environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results. Main conclusions The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate for studying niche differences between species, subspecies or intra-specific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the environmental niche of a species or intra-specific lineage has changed over time.

/pdf/measuring-ecological-niche-overlap-from-occurrence-and-4cw2471aer.pdf

Measuring ecological niche overlap from occurrence and spatial environmental data

The Atlantic bluefin tuna is at the centre of an international debate in fisheries conservation. Last summer the western Atlantic bluefin tuna fishery collapsed and some conservationists say it will not recover unless the International Commission for Conservation of Atlantic Tunas adopts a tougher regime. Results from an electronic tagging programme on bluefin tuna are presented this week, and they make alarming reading. Western tagged bluefin tuna are shown to migrate freely across the international stock boundary into the eastern Atlantic, where they are vulnerable to European fisheries; and both known spawning grounds, in the Gulf of Mexico and the Mediterranean Sea, are shown to be linked to the endangered western Atlantic fishery. Electronic tags that archive or transmit stored data to satellites have advanced the mapping of habitats used by highly migratory fish in pelagic ecosystems1,2,3,4,5,6. Here we report on the electronic tagging of 772 Atlantic bluefin tuna in the western Atlantic Ocean in an effort to identify population structure. Reporting electronic tags provided accurate location data7,8,9 that show the extensive migrations of individual fish (n = 330). Geoposition data delineate two populations, one using spawning grounds in the Gulf of Mexico and another from the Mediterranean Sea. Transatlantic movements of western-tagged bluefin tuna reveal site fidelity to known spawning areas in the Mediterranean Sea. Bluefin tuna that occupy western spawning grounds move to central and eastern Atlantic foraging grounds. Our results are consistent with two populations of bluefin tuna with distinct spawning areas that overlap on North Atlantic foraging grounds. Electronic tagging locations, when combined with US pelagic longline observer and logbook catch data, identify hot spots for spawning bluefin tuna in the northern slope waters of the Gulf of Mexico. Restrictions on the time and area where longlining occurs would reduce incidental catch mortalities on western spawning grounds.

Electronic tagging and population structure of Atlantic bluefin tuna

In this paper kernel methods for the nonparametric estimation of the utilization distribution from a random sample of locational observations made on an animal in its home range are described. They are of flexible form, thus can be used where simple parametric models are found to be inappropriate or difficult to specify. Two examples are given to illustrate the fixed and adaptive kernel approaches in data analysis and to compare the methods. Various choices for the smoothing parameter used in kernel methods are discussed. Since kernel methods give alternative approaches to the Anderson (1982) Fourier transform methods, some comparisons are made.

B. J. Worton

Papers

Kernel methods for estimating the utilization distribution in home-range studies